PAC Learning is just Bipartite Matching (Sort of)

TL;DR

This paper reveals a surprising connection between PAC learning and bipartite matching, using transductive models and one-inclusion graphs to deepen understanding of learning theory.

Contribution

It introduces a novel perspective linking PAC learning to bipartite matching through transductive models and graph-based approaches.

Findings

PAC learning relates closely to bipartite matching.

Transductive models and one-inclusion graphs generalize hat puzzles.

The approach offers new insights into deep learning theory questions.

Abstract

The main goal of this article is to convince you, the reader, that supervised learning in the Probably Approximately Correct (PAC) model is closely related to -- of all things -- bipartite matching! En-route from PAC learning to bipartite matching, I will overview a particular transductive model of learning, and associated one-inclusion graphs, which can be viewed as a generalization of some of the hat puzzles that are popular in recreational mathematics. Whereas this transductive model is far from new, it has recently seen a resurgence of interest as a tool for tackling deep questions in learning theory. A secondary purpose of this article could be as a (biased) tutorial on the connections between the PAC and transductive models of learning.